Applications of likelihood-free parameter inference methods on numerical models of cancer invasion (thesis data)

Yunchen Xiao (Creator)
Mark Chaplain (Supervisor)
Len Thomas (Supervisor)

Dataset

Description

Spatial 1D data/digital output of the thesis

Date made available	16 Aug 2022
Publisher	University of St Andrews

Contact

[email protected]

DOI
10.17630/f2a34bdc-d9a0-4dcf-8eb1-9d6c1a79af95

Access Dataset

Spatial_1D_data_part_1.zip
File: application/zip, 1.18 GB
Type: Dataset
Licence: CC BY
Spatial_1D_data_part_2.zip
File: application/zip, 2.83 GB
Type: Dataset
Licence: CC BY
Spatial_2D_data_part_1.zip
File: application/zip, 637 MB
Type: Dataset
Licence: CC BY
Spatial_2D_data_part_2.zip
File: application/zip, 610 MB
Type: Dataset
Licence: CC BY
Spatial_2D_data_part_3.zip
File: application/zip, 344 MB
Type: Dataset
Licence: CC BY

Related PhD Thesis

Calibrating models of cancer invasion: parameter estimation using approximate Bayesian computation and gradient matching
Xiao, Y., Thomas, L. & Chaplain, M. A. J., 16 Jun 2021, In: Royal Society Open Science. 8, 6, 17 p., 202237.
Research output: Contribution to journal › Article › peer-review

Open Access
File

Applications of likelihood-free parameter inference methods on numerical models of cancer invasion
Xiao, Y. (Author), Chaplain, M. A. J. (Supervisor) & Thomas, L. J. (Supervisor), 29 Nov 2022
Student thesis: Doctoral Thesis (PhD)

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